A review on estimation of stochastic differential equations for pharmacokinetic/pharmacodynamic models
AbstractThis paper is a survey of existing estimation methods for pharmacokinetic/pharmacodynamic (PK/PD) models based on stochastic differential equations (SDEs). Most parametric estimation methods proposed for SDEs require high frequency data and are often poorly suited for PK/PD data which are usually sparse. Moreover, PK/PD experiments generally include not a single individual but a group of subjects, leading to a population estimation approach. This review concentrates on estimation methods which have been applied to PK/PD data, for SDEs observed with and without measurement noise, with a standard or a population approach. Besides, the adopted methodologies highly differ depending on the existence or not of an explicit transition density of the SDE solution.
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Bibliographic InfoPaper provided by Paris Dauphine University in its series Economics Papers from University Paris Dauphine with number 123456789/11429.
Date of creation: 2013
Date of revision:
Publication status: Published in Advanced Drug Delivery Reviews, 2013, Vol. 65, no. 7. pp. 929-939.Length: 10 pages
Stochastic differential equations; Pharmacokinetic; Pharmacodynamic; population approach; maximum likelihood estimation; Kalman Filter; EM algorithm; Hermite expansion; Gauss quadrature; Bayesian estimation;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-11-16 (All new papers)
- NEP-ECM-2013-11-16 (Econometrics)
- NEP-HEA-2013-11-16 (Health Economics)
- NEP-ORE-2013-11-16 (Operations Research)
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